A Variable Photo-Model Method for Object Pose and Size Estimation with Stereo Vision in a Complex Home Scene.

Model-based stereo vision methods can estimate the 6D poses of rigid objects. They can help robots to achieve a target grip in complex home environments. This study presents a novel approach, called the variable photo-model method, to estimate the pose and size of an unknown object using a single photo of the same category. By employing a pre-trained You Only Look Once (YOLO) v4 weight for object detection and 2D model generation in the photo, the method converts the segmented 2D photo-model into 3D flat photo-models assuming different sizes and poses. Through perspective projection and model matching, the method finds the best match between the model and the actual object in the captured stereo images. The matching fitness function is optimized using a genetic algorithm (GA). Unlike data-driven approaches, this approach does not require multiple photos or pre-training time for single object pose recognition, making it more versatile. Indoor experiments demonstrate the effectiveness of the variable photo-model method in estimating the pose and size of the target objects within the same class. The findings of this study have practical implications for object detection prior to robotic grasping, particularly due to its ease of application and the limited data required.

Silencing of transcription factor encoding gene StTCP23 by small RNAs derived from the virulence modulating region of potato spindle tuber viroid is associated with symptom development in potato.

Viroids are small, non-protein-coding RNAs which can induce disease symptoms in a variety of plant species. Potato (Solanum tuberosum L.) is the natural host of Potato spindle tuber viroid (PSTVd) where infection results in stunting, distortion of leaves and tubers and yield loss. Replication of PSTVd is accompanied by the accumulation of viroid-derived small RNAs (sRNAs) proposed to play a central role in disease symptom development. Here we report that PSTVd sRNAs direct RNA silencing in potato against StTCP23, a member of the TCP (teosinte branched1/Cycloidea/Proliferating cell factor) transcription factor family genes that play an important role in plant growth and development as well as hormonal regulation, especially in responses to gibberellic acid (GA). The StTCP23 transcript has 21-nucleotide sequence complementarity in its 3' untranslated region with the virulence-modulating region (VMR) of PSTVd strain RG1, and was downregulated in PSTVd-infected potato plants. Analysis using 3' RNA ligase-mediated rapid amplification of cDNA ends (3' RLM RACE) confirmed cleavage of StTCP23 transcript at the expected sites within the complementarity with VMR-derived sRNAs. Expression of these VMR sRNA sequences as artificial miRNAs (amiRNAs) in transgenic potato plants resulted in phenotypes reminiscent of PSTVd-RG1-infected plants. Furthermore, the severity of the phenotypes displayed was correlated with the level of amiRNA accumulation and the degree of amiRNA-directed down-regulation of StTCP23. In addition, virus-induced gene silencing (VIGS) of StTCP23 in potato also resulted in PSTVd-like phenotypes. Consistent with the function of TCP family genes, amiRNA lines in which StTCP23 expression was silenced showed a decrease in GA levels as well as alterations to the expression of GA biosynthesis and signaling genes previously implicated in tuber development. Application of GA to the amiRNA plants minimized the PSTVd-like phenotypes. Taken together, our results indicate that sRNAs derived from the VMR of PSTVd-RG1 direct silencing of StTCP23 expression, thereby disrupting the signaling pathways regulating GA metabolism and leading to plant stunting and formation of small and spindle-shaped tubers.

Surface Defects Detection of Stamping and Grinding Flat Parts Based on Machine Vision.

Currently, surface defect detection of stamping grinding flat parts is mainly undertaken through observation by the naked eye. In order to improve the automatic degree of surface defects detection in stamping grinding flat parts, a real-time detection system based on machine vision is designed. Under plane illumination mode, the whole region of the parts is clear and the outline is obvious, but the tiny defects are difficult to find; Under multi-angle illumination mode, the tiny defects of the parts can be highlighted. In view of the above situation, a lighting method combining plane illumination mode with multi-angle illumination mode is designed, and five kinds of defects are automatically detected by different detection methods. Firstly, the parts are located and segmented according to the plane light source image, and the defects are detected according to the gray anomaly. Secondly, according to the surface of the parts reflective characteristics, the influence of the reflection on the image is minimized by adjusting the exposure time of the camera, and the position and direction of the edge line of the gray anomaly region of the multi-angle light source image are used to determine whether the anomaly region is a defect. The experimental results demonstrate that the system has a high detection success rate, which can meet the real-time detection rEquation uirements of a factory.

Alterations in vaginal temperature during the estrous cycle in dairy cows detected by a new intravaginal device-a pilot study.

Estrus identification is important in dairy cow production. At present, estrus identification is automated with a pedometer or accelerometer and the results remain unsatisfactory. It was previously reported that body temperature changes during estrus. In the present study, dairy cow vaginal temperature (VT) was monitored during various seasons, and an increase in VT of 0.3 °C was suggested for the onset of estrus, using an automated VT monitoring system developed in-house. Natural and synchronized estrus were measured simultaneously. The VT was determined to be in circadian rhythm and significantly higher in summer than in either autumn or winter (P < 0.05). VT difference (between estrus VT and average VT 7 days earlier) gradually increased, reached a peak of 0.56 °C ± 0.17 at 4 h before the end of estrus, and then decreased to the normal. The VT of cows in estrus and the duration of their estrus were significantly affected by seasons and estrus types (P < 0.05). VT gradually decreased in response to prostaglandin (PG) injection and was significantly lower (0.15-0.35 °C) from 9 to 33 h after the drug administration than the average VT at the same time 7 days earlier (P < 0.05). Changes in circadian and seasonal VT and in the estrous cycle can be monitored to assess the physiological status of cows and will help in developing an effective automated estrus identification technique. Results of this pilot study should be validated in further studies.

Can Digital Finance Promote Peak Carbon Dioxide Emissions? Evidence from China.

This paper uses Chinese provincial panel data from 2011 to 2019, measures CO2 emissions of provinces in China using the IPCC method, and explores the impact of digital finance on CO2 emissions through the SAR model and SDM. Empirical study shows that digital finance significantly reduces CO2 emissions. Digital finance reduces CO2 emissions by promoting energy industrial structure transformation and spreads to surrounding areas through spillover effects, contributes to increasing green patents granted and thus reduces regional CO2 emissions, advances the green technological progress and therefore inhibits CO2 emissions, but reduces the green technological progress in surrounding areas and increases CO2 emissions due to the siphon effect. With the development of digital finance itself, the higher the level of financial regulation, green development and the green finance index, the better the effect of digital finance on CO2 emission reduction. Additionally, digital finance significantly reduces CO2 emissions in the south of China.